I was walking along the sea this morning, listening to Marcant—a German anti-far-right activist and content creator—being interviewed on Hotel Matze, one of Germany's most widely listened-to podcasts.
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Something I've been noticing in a lot of my coaching conversations lately: Decisions feel harder right now. Not because product leaders suddenly got worse at making them. If anything, making decisions with incomplete information has always been one of the core skills of this job. You rarely have all the data. You're used to operating in ambiguity. That's not new.
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You've probably had this conversation recently: a stakeholder, maybe even your CEO, pulls you aside and asks, "What do we need to consider now to make our organization AI future-ready?"
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Welcome to autumn! We’re kicking off the new season with a hot topic on the All Things Product podcast: AI evals and discovery. In just a few days, this episode has come close to breaking our record of the most views on YouTube, so clearly it’s a topic that’s resonating.
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We’re nearing the end of our summer break and I wanted to take a moment to share what was clearly the most popular episode of the All Things Product podcast this summer: AI Prototyping. Given the interest in this topic in the product world in general (and even in our own programming at Product at Heart), this is not surprising at all.
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If you’re a product leader quietly wondering whether you’re already behind on AI, you’re not alone. Many leaders I coach feel the same: overwhelmed by the expectations, unsure where to start, and guilty for not leading their teams more proactively. They’ve read some articles and books, but don’t feel they have the practical experience yet to confidently set direction. And meanwhile, stakeholders and peers are pushing: Where is our AI strategy? Where is it on the roadmap?
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